Learning of associative memory in form of neural network suitable for connectionist model
First Claim
1. An apparatus, comprising:
- an associative memory in a form of a neural network, including;
a plurality of nodes having activation values; and
a plurality of links, connected with the nodes, having link weight values;
pattern entering means for sequentially entering a plurality of learning patterns into the neural network, each learning pattern having a plurality of elements in correspondence with the nodes;
energy calculation means for calculating an energy E of said each learning pattern entered by the pattern entering means;
learning amount determination means for determining a learning amount δ
for said each learning pattern entered by the pattern entering means, according to a difference between the energy E calculated by the energy calculation means and a predetermined reference energy level Eth, using the following equation;
space="preserve" listing-type="equation">δ
=g(E-Eth) where g is an upper and lower bounded monotonically increasing function with g(0)=0; and
link weight value updating means for updating the link weight values of the links according to said each learning pattern entered by the pattern entering means and the learning amount δ
determined by the learning amount determination means.
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Abstract
The learning of an associative memory suitable for the connectionist model which can deal with the patterns having the non-random frequencies of the appearances or the non-random correlations. In this invention, the learning of the associative memory in a form of a neural network, in which a plurality of nodes having activation values are connected by a plurality of links having link weight values, is achieved by entering a plurality of learning patterns sequentially, where each learning pattern has a plurality of elements in correspondence with the nodes, calculating an energy E of the entered learning pattern, determining a learning amount δ for the entered learning pattern according to a difference between the calculated energy E and a predetermined reference energy level Eth, and updating the link weight values of the links according to the entered learning pattern and the determined learning amount δ.
88 Citations
18 Claims
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1. An apparatus, comprising:
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an associative memory in a form of a neural network, including; a plurality of nodes having activation values; and a plurality of links, connected with the nodes, having link weight values; pattern entering means for sequentially entering a plurality of learning patterns into the neural network, each learning pattern having a plurality of elements in correspondence with the nodes; energy calculation means for calculating an energy E of said each learning pattern entered by the pattern entering means; learning amount determination means for determining a learning amount δ
for said each learning pattern entered by the pattern entering means, according to a difference between the energy E calculated by the energy calculation means and a predetermined reference energy level Eth, using the following equation;
space="preserve" listing-type="equation">δ
=g(E-Eth)where g is an upper and lower bounded monotonically increasing function with g(0)=0; and link weight value updating means for updating the link weight values of the links according to said each learning pattern entered by the pattern entering means and the learning amount δ
determined by the learning amount determination means. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of operating an associative memory in a form of a neural network in which a plurality of nodes having activation values are connected by a plurality of links having link weight values, comprising the steps of:
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(a) entering a plurality of learning patterns into the neural network sequentially, each learning pattern having a plurality of elements in correspondence with the nodes; (b) calculating an energy E of said each learning pattern entered at the step (a); (c) determining a learning amount δ
for said each learning pattern according to a difference between the energy E calculated at the step (b) and a predetermined reference energy level Eth, using the following equation;
space="preserve" listing-type="equation">δ
=g(E-Eth)where g is an upper and lower bounded monotonically increasing function with g(0)=0; (d) updating the link weight values of the links in the neural network according to said each learning pattern entered at the step (a) and the learning amount δ
determined at the step (c), so as to achieve a learning of the associative memory;(e) presenting an input pattern having a plurality of elements in correspondence with the nodes to the neural network; (f) updating the activation values of the nodes in the neural network according to the input pattern presented at the step (e) and the link weight values of the links in the neural network updated at the step (d); and (g) operating the associative memory for recalling an appropriate pattern according to the activation values of the nodes in the neural network updated at the step (f), so as to achieve an association by the associative memory. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18)
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Specification